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Norinder U.,Swedish Toxicology science Research Center | Boyer S.,Swedish Toxicology science Research Center
Journal of Molecular Graphics and Modelling | Year: 2017

Aggregated Conformal Prediction is used as an effective alternative to other, more complicated and/or ambiguous methods involving various balancing measures when modelling severely imbalanced datasets. Additional explicit balancing measures other than those already apart of the Conformal Prediction framework are shown not to be required. The Aggregated Conformal Prediction procedure appears to be a promising approach for severely imbalanced datasets in order to retrieve a large majority of active minority class compounds while avoiding information loss or distortion. © 2017 Elsevier Inc.


Norinder U.,Swedish Toxicology science Research Center | Carlsson L.,Astrazeneca | Boyer S.,Swedish Toxicology science Research Center | Eklund M.,Astrazeneca | Eklund M.,University of California at San Francisco
Regulatory Toxicology and Pharmacology | Year: 2015

Conformal prediction is presented as a framework which fulfills the OECD principles on (Q)SAR. It offers an intuitive extension to the application of machine-learning methods to structure-activity data where focus is on predictions with pre-defined confidence levels. A conformal predictor will make correct predictions on new compounds corresponding to a user defined confidence level. The confidence level can be altered depending on the situation the predictor is being used in, which allows for flexibility and adaption to risks that the user is willing to take. We demonstrate the usefulness of conformal prediction by applying it to 2 publicly available CAESAR binary classification datasets. © 2015 Elsevier Inc.


Norinder U.,Lundbeck | Carlsson L.,Astrazeneca | Boyer S.,Astrazeneca | Boyer S.,Swedish Toxicology science Research Center | And 2 more authors.
Journal of Chemical Information and Modeling | Year: 2014

Conformal prediction is introduced as an alternative approach to domain applicability estimation. The advantages of using conformal prediction are as follows: First, the approach is based on a consistent and well-defined mathematical framework. Second, the understanding of the confidence level concept in conformal predictions is straightforward, e.g. a confidence level of 0.8 means that the conformal predictor will commit, at most, 20% errors (i.e., true values outside the assigned prediction range). Third, the confidence level can be varied depending on the situation where the model is to be applied and the consequences of such changes are readily understandable, i.e. prediction ranges are increased or decreased, and the changes can immediately be inspected. We demonstrate the usefulness of conformal prediction by applying it to 10 publicly available data sets. © 2014 American Chemical Society.


PubMed | Swedish Toxicology science Research Center, Karolinska Institutet, Lancaster University, University of Edinburgh and 16 more.
Type: Journal Article | Journal: Environmental health : a global access science source | Year: 2016

The issue of endocrine disrupting chemicals (EDCs) is receiving wide attention from both the scientific and regulatory communities. Recent analyses of the EDC literature have been criticized for failing to use transparent and objective approaches to draw conclusions about the strength of evidence linking EDC exposures to adverse health or environmental outcomes. Systematic review methodologies are ideal for addressing this issue as they provide transparent and consistent approaches to study selection and evaluation. Objective methods are needed for integrating the multiple streams of evidence (epidemiology, wildlife, laboratory animal, in vitro, and in silico data) that are relevant in assessing EDCs.We have developed a framework for the systematic review and integrated assessment (SYRINA) of EDC studies. The framework was designed for use with the International Program on Chemical Safety (IPCS) and World Health Organization (WHO) definition of an EDC, which requires appraisal of evidence regarding 1) association between exposure and an adverse effect, 2) association between exposure and endocrine disrupting activity, and 3) a plausible link between the adverse effect and the endocrine disrupting activity.Building from existing methodologies for evaluating and synthesizing evidence, the SYRINA framework includes seven steps: 1) Formulate the problem; 2) Develop the review protocol; 3) Identify relevant evidence; 4) Evaluate evidence from individual studies; 5) Summarize and evaluate each stream of evidence; 6) Integrate evidence across all streams; 7) Draw conclusions, make recommendations, and evaluate uncertainties. The proposed method is tailored to the IPCS/WHO definition of an EDC but offers flexibility for use in the context of other definitions of EDCs.When using the SYRINA framework, the overall objective is to provide the evidence base needed to support decision making, including any action to avoid/minimise potential adverse effects of exposures. This framework allows for the evaluation and synthesis of evidence from multiple evidence streams. Finally, a decision regarding regulatory action is not only dependent on the strength of evidence, but also the consequences of action/inaction, e.g. limited or weak evidence may be sufficient to justify action if consequences are serious or irreversible.


PubMed | Swedish Toxicology science Research Center and Linköping University
Type: | Journal: Environment international | Year: 2016

There is a growing body of evidence that persistent organic pollutants (POPs) may increase the risk for cardiovascular disease (CVD), but the mechanisms remain unclear. High-density lipoprotein (HDL) acts protective against CVD by different processes, and we have earlier found that HDL from subjects with CVD contains higher levels of POPs than healthy controls. In the present study, we have expanded analyses on the same individuals living in a contaminated community and investigated the relationship between the HDL POP levels and protein composition/function. HDL from 17 subjects was isolated by ultracentrifugation. HDL protein composition, using nanoliquid chromatography tandem mass spectrometry, and antioxidant activity were analyzed. The associations of 16 POPs, including polychlorinated biphenyls (PCBs) and organochlorine pesticides, with HDL proteins/functions were investigated by partial least square and multiple linear regression analysis. Proteomic analyses identified 118 HDL proteins, of which ten were significantly (p<0.05) and positively associated with the combined level of POPs or with highly chlorinated PCB congeners. Among these, cholesteryl ester transfer protein and phospholipid transfer protein, as well as the inflammatory marker serum amyloid A, were found. The serum paraoxonase/arylesterase 1 activity was inversely associated with POPs. Pathway analysis demonstrated that up-regulated proteins were associated with biological processes involving lipoprotein metabolism, while down-regulated proteins were associated with processes such as negative regulation of proteinases, acute phase response, platelet degranulation, and complement activation. These results indicate an association between POP levels, especially highly chlorinated PCBs, and HDL protein alterations that may result in a less functional particle. Further studies are needed to determine causality and the importance of other environmental factors. Nevertheless, this study provides a first insight into a possible link between exposure to POPs and risk of CVD.


Grimm F.A.,University of Iowa | Hu D.,University of Iowa | Kania-Korwel I.,University of Iowa | Lehmler H.-J.,University of Iowa | And 5 more authors.
Critical Reviews in Toxicology | Year: 2015

The metabolism of polychlorinated biphenyls (PCBs) is complex and has an impact on toxicity, and thereby on the assessment of PCB risks. A large number of reactive and stable metabolites are formed in the processes of biotransformation in biota in general, and in humans in particular. The aim of this document is to provide an overview of PCB metabolism, and to identify the metabolites of concern and their occurrence. Emphasis is given to mammalian metabolism of PCBs and their hydroxyl, methylsulfonyl, and sulfated metabolites, especially those that persist in human blood. Potential intracellular targets and health risks are also discussed. © 2015 Informa Healthcare USA, Inc.


PubMed | Swedish Toxicology science Research Center and Gothenburg University
Type: | Journal: Aquatic toxicology (Amsterdam, Netherlands) | Year: 2016

Zebrafish (Danio rerio) is not only a widely used species in the Fish Embryo Toxicity (FET) test but also an emerging model in behavioural ecotoxicology. By using automatic behaviour tracking technology, locomotion of developing zebrafish (ZF) larvae can be accurately recorded and potentially used in an ecotoxicological context to detect toxicant-induced behavioural alterations. In this study, we explored if and how quantitative locomotion data can be used for sub-lethal toxicity testing within the FET framework. We exposed ZF embryos to silver ions and nanoparticles, which previously have been reported to cause neurodevelopmental toxicity and behavioural retardation in early-life stages of ZF. Exposure to a broad range of silver (Ag(+) and AgNPs) concentrations was conducted, and developmental toxicity was assessed using FET criteria. For behavioural toxicity assessment, locomotion of exposed ZF eleutheroembryos (120hpf) was quantified according to a customised behavioural assay in an automatic video tracking system. A set of repeated episodes of dark/light stimulation were used to artificially stress ZF and evoke photo-motor responses, which were consequently utilized for locomotion profiling. Our locomotion-based behaviour profiling approach consisted of (1) dose-response ranking for multiple and single locomotion variables; (2) quantitative assessment of locomotion structure; and (3) analysis of ZF responsiveness to darkness stimulation. We documented that both silver forms caused adverse effects on development and inhibited hatchability and, most importantly, altered locomotion. High Ag(+) and AgNPs exposures significantly suppressed locomotion and a clear shift in locomotion towards inactivity was reported. Additionally, we noted that low, environmentally relevant Ag(+) concentrations may cause subordinate locomotive changes (hyperactivity) in developing fish. Overall, it was concluded that our locomotion-based behaviour-testing scheme can be used jointly with FET and can provide endpoints for sub-lethal toxicity. When combined with multivariate data analysis, this approach facilitated new insights for handling and analysis of data generated by automatized behavioural tracking systems.


Ahlberg E.,Astrazeneca | Carlsson L.,Astrazeneca | Boyer S.,Astrazeneca | Boyer S.,Swedish Toxicology science Research Center
Journal of Chemical Information and Modeling | Year: 2014

Structural alerts have been one of the backbones of computational toxicology and have applications in many areas including cosmetic, environmental, and pharmaceutical toxicology. The development of structural alerts has always involved a manual analysis of existing data related to a relevant end point followed by the determination of substructures that appear to be related to a specific outcome. The substructures are then analyzed for their utility in posterior validation studies, which at times have stretched over years or even decades. With higher throughput methods now being employed in many areas of toxicology, data sets are growing at an unprecedented rate. This growth has made manual analysis of data sets impractical in many cases. This report outlines a fully automatic method that highlights significant substructures for toxicologically important data sets. The method identifies important substructures by computationally breaking chemical structures into fragments and analyzing those fragments for their contribution to the given activity by the calculation of a p-value and a substructure accuracy. The method is intended to aid the expert in locating and analyzing alerts by automatic retrieval of alerts or by enhancing existing alerts. The method has been applied to a data set of AMES mutagenicity results and compared to the substructures generated by manual curation of this same data set as well as another computationally based substructure identification method. The results show that this method can retrieve significant substructures quickly, that the substructures are comparable and in some cases superior to those derived from manual curation, that the substructures found covers all previously known substructures, and that they can be used to make reasonably accurate predictions of AMES activity. © 2014 American Chemical Society.


PubMed | Swedish Toxicology science Research Center
Type: Journal Article | Journal: Chemical research in toxicology | Year: 2016

Quantitative structure-activity relationships (QSAR) are critical to exploitation of the chemical information in toxicology databases. Exploitation can be extraction of chemical knowledge from the data but also making predictions of new chemicals based on quantitative analysis of past findings. In this study, we analyzed the ToxCast and Tox21 estrogen receptor data sets using Conformal Prediction to enhance the full exploitation of the information in these data sets. We applied aggregated conformal prediction (ACP) to the ToxCast and Tox21 estrogen receptor data sets using support vector machine classifiers to compare overall performance of the models but, more importantly, to explore the performance of ACP on data sets that are significantly enriched in one class without employing sampling strategies of the training set. ACP was also used to investigate the problem of applicability domain using both data sets. Comparison of ACP to previous results obtained on the same data sets using traditional QSAR approaches indicated similar overall balanced performance to methods in which careful training set selections were made, e.g., sensitivity and specificity for the external Tox21 data set of 70-75% and far superior results to those obtained using traditional methods without training set sampling where the corresponding results showed a clear imbalance of 50 and 96%, respectively. Application of conformal prediction to imbalanced data sets facilitates an unambiguous analysis of all data, allows accurate predictive models to be built which display similar accuracy in external validation to external validation, and, most importantly, allows an unambiguous treatment of the applicability domain.


PubMed | Swedish Toxicology science Research Center
Type: | Journal: Journal of molecular graphics & modelling | Year: 2017

Aggregated Conformal Prediction is used as an effective alternative to other, more complicated and/or ambiguous methods involving various balancing measures when modelling severely imbalanced datasets. Additional explicit balancing measures other than those already apart of the Conformal Prediction framework are shown not to be required. The Aggregated Conformal Prediction procedure appears to be a promising approach for severely imbalanced datasets in order to retrieve a large majority of active minority class compounds while avoiding information loss or distortion.

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